Skip to main content
SpringerLink
Log in

Evaluation of Empirical Design Studies and Metrics

  • Chapter
  • First Online:
Experimental Design Research

Abstract

Engineering design is a complex multifaceted and knowledge-intensive process. No single theory or model can capture all aspects of such an activity. Various empirical methods have been used by researchers to study particular aspects of design thinking and cognition, design processes, design artefacts, and design strategies. Research methods include think-aloud protocol analysis and its many variants, case studies, controlled experiments of design cognition, and fMRI. The field has gradually progressed from subjective to objective analyses, requiring well-defined metrics since design of experiments (DOE) involves controlling or blocking particular variables. DOE also requires setting experiment variables at particular levels, which means that each variable needs to be characterized and quantified. Without such quantification, statistical analyses cannot be carried out. This chapter focuses on quantifiable characteristics of designers, targeted users, artefacts, and processes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahn J, Crawford R (1994) Complexity analysis of computational engineering design. In: ASME international design engineering technical conferences and computers and information in engineering conference. ASME, Minneapolis, pp 205–220

    Google Scholar 

  • Amabile T (1996) Creativity in context: update to the social psychology of creativity. Westview Press, Boulder

    Google Scholar 

  • Ameri F, Summers JD, Mocko GM, Porter M (2008) Engineering design complexity: an investigation of methods and measures. Res Eng Des 19:161–179

    Article  Google Scholar 

  • Arrighi P, Le Masson P, Weil B (2015) Addressing constraints creatively: how new design software helps solve the dilemma of originality and feasibility. Creat Innov Manag 24:247–260

    Article  Google Scholar 

  • Arrow KJ (1950) A difficulty in the concept of social welfare. J Polit Econ 58:328–346

    Google Scholar 

  • Balazs M, Brown D (2002) Design simplification by analogical reasoning. In: From knowledge intensive CAD to knowledge intensive engineering. Kluwer Academic Publishers, Norwell, pp 29–44

    Google Scholar 

  • Barzilai J (2006) Preference modeling in engineering design. In: Lewis KE, Chen W, Schmidt LC (eds) ASME. New York, USA

    Google Scholar 

  • Bashir HA, Thomson V (2004) Estimating design effort for GE hydro project. Comput Ind Eng 46:195–204

    Article  Google Scholar 

  • Bashir HA, Thomson V (2001) An analogy-based model for estimating design effort. Des Stud 22:157–167

    Article  Google Scholar 

  • Bearden DA (2003) A complexity-based risk assessment of low-cost planetary missions: when is a mission too fast and too cheap? Acta Astronaut 52:371–379

    Article  Google Scholar 

  • Boothroyd G, Dewhurst P, Knight W (2002) Product design for manufacture and assembly. M. Dekker, New York

    Google Scholar 

  • Braha D, Maimon O (1998a) The measurement of a design structural and functional complexity. IEEE Trans Syst Man Cybern (Part A—Syst Hum) 28:527–535

    Google Scholar 

  • Braha D, Maimon O (1998b) A mathematical theory of design: foundations, algorithms, and applications. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Callaghan A, Lewis K (2000) A 2-phase aspiration-level and utility theory approach to large scale design. In: ASME design automation conference, Baltimore, MD, DETC00/DTM-14569. Citeseer

    Google Scholar 

  • Cham JG, Yang MC (2005) Does sketching skill relate to good design? In: Proceedings of ASME DETC. Long Beach, CA

    Google Scholar 

  • Chen W, Hoyle C, Wassenaar HJ (2012) Decision-based design: integrating consumer preferences into engineering design. Springer Science & Business Media

    Google Scholar 

  • Chen W, Hoyle C, Wassenaar HJ (2013) Hierarchical choice modeling to support complex systems design. In: Decision-based design. Springer, pp 205–233

    Google Scholar 

  • Cheng P, Mugge R, Schoormans JPL (2014) A new strategy to reduce design fixation: presenting partial photographs to designers. Des Stud 35:374–391

    Article  Google Scholar 

  • Chiu I, Salustri FA (2010) Evaluating design project creativity in engineering design courses

    Google Scholar 

  • Chulvi V, González-Cruz MC, Mulet E, Aguilar-Zambrano J (2012a) Influence of the type of idea-generation method on the creativity of solutions. Res Eng Des 1–9

    Google Scholar 

  • Chulvi V, Mulet E, Chakrabarti A et al (2012b) Comparison of the degree of creativity in the design outcomes using different design methods. J Eng Des 23:241–269

    Google Scholar 

  • Cross N (1997) Creativity in design: analyzing and modeling the creative leap. Leonardo 30:311–317

    Google Scholar 

  • Deerwester S, Dumais ST, Furnas GW et al (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41:391–407

    Article  Google Scholar 

  • Dinar M, Shah JJ (2014) Enhancing design problem formulation skills for engineering design students. In: Proceedings of ASME IDETC/CIE. ASME, Buffalo

    Google Scholar 

  • Dinar M, Danielescu A, MacLellan C et al (2015a) Problem Map: an ontological framework for a computational study of problem formulation in engineering design. J Comput Inf Sci Eng 15:1–10. doi:10.1115/1.4030076

    Google Scholar 

  • Dinar M, Park Y-S, Shah JJ (2015b) Evaluating the effectiveness of problem formulation and ideation skills learned throughout an engineering design course. In: Proceedings of ASME IDETC/CIE, Boston, MA, USA

    Google Scholar 

  • Dinar M, Park Y-S, Shah JJ, Langley P (2015c) Patterns of creative design: predicting ideation from problem formulation. In: Proceedings of ASME IDETC/CIE, Boston, MA, USA

    Google Scholar 

  • Dinar M, Shah JJ, Todeti SR (2015d) Towards a comprehensive test of problem formulation skill in design. In: Chakrabarti A, Taura T, Nagai Y (eds) Proceedings of the third international conference on design creativity. Bangalore, India, pp 19–26

    Google Scholar 

  • Dixon J, Duffey M, Irani R et al (1988) A proposed taxonomy of mechanical design problems. Computers in engineering conference. ASME, New York, pp 41–46

    Google Scholar 

  • Dong A, Hill AW, Agogino AM (2004) A document analysis method for characterizing design team performance. J Mech Des 126:378–385. doi:10.1115/1.1711818

    Article  Google Scholar 

  • Du D, Ko K (2000) Theory of computational complexity. John Wiley and Sons, New York

    Book  MATH  Google Scholar 

  • Dym CL, Wood WH, Scott MJ (2002) Rank ordering engineering designs: pairwise comparison charts and Borda counts. Res Eng Des 13:236–242. doi:10.1007/s00163-002-0019-8

    Google Scholar 

  • El-Haik B, Yang K (1999) The components of complexity in engineering design. IIE Trans 31:925–934

    Google Scholar 

  • Eris O (2002) Perceiving, comprehending, and measuring design activity through the questions asked while designing, Stanford University

    Google Scholar 

  • Eris O (2004) Effective inquiry for innovative engineering design. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  • Fishburn PC (1967) Additive utilities with incomplete product set: applications to priorities and sharings

    Google Scholar 

  • Fitzhorn P (1994) Engineering design as a computable function. Artif Intell Eng Des Anal Manuf 8:35–44

    Article  Google Scholar 

  • Fu K, Cagan J, Kotovsky K (2010) Design team convergence: the influence of example solution quality. J Mech Des 132:111005. doi:10.1115/1.4002202

    Article  Google Scholar 

  • Fu K, Cagan J, Kotovsky K, Wood KL (2013a) Discovering structure in design databases through functional and surface based mapping. J Mech Des 135:031006. doi:10.1115/1.4023484

    Google Scholar 

  • Fu K, Chan J, Cagan J et al (2013b) The meaning of “Near” and “Far”: the impact of structuring design databases and the effect of distance of analogy on design output. J Mech Des 135:021007. doi:10.1115/1.4023158

    Google Scholar 

  • Glier MW, McAdams DA, Linsey JS (2014) Exploring automated text classification to improve keyword corpus search results for bioinspired design. J Mech Des 136:111103. doi:10.1115/1.4028167

    Google Scholar 

  • Green M, Seepersad CC, Hölttä-Otto K (2014) Crowd-sourcing the evaluation of creativity in conceptual design: a pilot study. In: Proceedings of ASME IDETC/CIE. ASME, Portland, p V007T07A016

    Google Scholar 

  • Green PE, Carmone FJ (1970) Multidimensional scaling and related techniques in marketing analysis. Allyn and Bacon, Boston

    Google Scholar 

  • Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. J Mark 54:3–19

    Google Scholar 

  • Green PE, Srinivasan V (1978) Conjoint analysis in consumer research: issues and outlook. J Consum Res 5:103–123

    Google Scholar 

  • Green PE, Tull DS (1970) Research for marketing decisions

    Google Scholar 

  • Gu X, Renaud JE, Ashe LM et al (2002) Decision-based collaborative optimization. J Mech Des 124:1–13

    Article  Google Scholar 

  • Hamade R (2009) Profiling the desirable CAD trainee: technical background, personality attributes, and learning preferences. ASME Trans J Mech Des 131:121–130

    Article  Google Scholar 

  • Hannah R, Joshi S, Summers JD (2011) A user study of interpretability of engineering design representations. J Eng Des 23:443–468. doi:10.1080/09544828.2011.615302

    Article  Google Scholar 

  • Harrison W, Magel K (1981) A complexity measure based on nesting level. SIGPLAN Not 16:63–74

    Article  Google Scholar 

  • Hazelrigg GA (1996) The implications of Arrow’s impossibility theorem on approaches to optimal engineering design. Trans Soc Mech Eng J Mech Des 118:161–164

    Google Scholar 

  • Ho C (2001) Some phenomena of problem decomposition strategy for design thinking: differences between novices and experts. Des Stud 22:27–45. doi:10.1016/S0142-694X(99)00030-7

    Article  Google Scholar 

  • Holtta K, Otto K (2005) Incorporating design effort complexity measures in product architectural design and assessment. Des Stud 26:463–485

    Article  Google Scholar 

  • Hoyle C, Chen W, Wang N, Koppelman FS (2010) Integrated Bayesian hierarchical choice modeling to capture heterogeneous consumer preferences in engineering design. J Mech Des 132:121010

    Article  Google Scholar 

  • Hoyle C, Chen W (2007) Next generation QFD: decision-based product attribute function deployment

    Google Scholar 

  • Hoyle CJ, Chen W (2009) Product attribute function deployment (PAFD) for decision-based conceptual design. Eng Manag IEEE Trans 56:271–284

    Article  Google Scholar 

  • Hunt BJ, Blouin VY, Wiecek MM (2007) Modeling relative importance of design criteria with a modified pareto preference. J Mech Des 129:907. doi:10.1115/1.2747634

    Article  Google Scholar 

  • Johnson RM (1970) Multiple discriminant analysis: applications to marketing research. Market Facts, Incorporated

    Google Scholar 

  • Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13(4):455–492

    Google Scholar 

  • Joshi S, Summers JD (2012) Representation: metrics for analyzing sketches. In: International design engineering technical conferences and computers and information in engineering conference, Chicago, IL, pp DETC2012–71425

    Google Scholar 

  • Khorshidi M, Shah JJ, Woodward J (2014) Applied tests of design skills—part III: abstract reasoning. J Mech Des 136:101101. doi:10.1115/1.4027986

    Article  Google Scholar 

  • Kogure M, Akao Y (1983) Quality function deployment and CWQC in Japan. Qual Prog 16:25–29

    Google Scholar 

  • Kolmogorov A (1983) Combinatorial foundations of information theory and the calculus of probabilities. Russ Math Surv 38:29–40

    Article  MathSciNet  MATH  Google Scholar 

  • Kudrowitz BM, Wallace D (2013) Assessing the quality of ideas from prolific, early-stage product ideation. J Eng Des 24:120–139. doi:10.1080/09544828.2012.676633

    Article  Google Scholar 

  • Landauer TK, Foltz PW, Laham D (1998) An introduction to latent semantic analysis. Discourse Process 25:259–284

    Article  Google Scholar 

  • Lau K, Oehlberg L, Agogino A (2009) Sketching in design journals: an analysis of visual representations in the product design process. Eng Des Graph J 73

    Google Scholar 

  • Le Dain M-A, Blanco E, Summers JD (2013) Assessing design research quality: investigating verification and validation criteria. In: International conference on engineering design. The Design Society, Seoul

    Google Scholar 

  • Lee JH, Gu N, Ostwald MJ (2015) Creativity and parametric design? Comparing designer’s cognitive approaches with assessed levels of creativity. Int J Des Creat Innov 3:78–94

    Article  Google Scholar 

  • Linsey JS, Clauss EF, Kurtoglu T et al (2011) An experimental study of group idea generation techniques: understanding the roles of idea representation and viewing methods. J Mech Des 133:031008. doi:10.1115/1.4003498

    Article  Google Scholar 

  • Linsey JS, Green MG, Murphy J, Wood K (2005a) Collaborating to Success: an experimental study of group idea generation techniques. In: Proceedings of the IDETC/CIE 2005 conference. ASME, Long Beach, CA, pp DETC2005–85351

    Google Scholar 

  • Linsey JS, Green MG, Van Wie M, et al (2005b) Functional representations in conceptual design: a first study in experimental design and evaluation. In: Proceedings of 2005 American society for engineering education annual conference. Citeseer

    Google Scholar 

  • Linsey JS, Viswanathan VK (2014) Overcoming cognitive challenges in bioinspired design and analogy. In: Biologically inspired design. Springer, pp 221–244

    Google Scholar 

  • Linsey JS, Wood KL, Markman AB (2008) Modality and representation in analogy. AI EDAM (Artificial Intell Eng Des Anal Manuf) 22:85

    Google Scholar 

  • Mathieson J, Wallace B, Summers JD (2013) Estimating Assembly Time with Connective Complexity Metric Based Surrogate Models. Int J Comput Integr Manuf 26:955–967. doi:10.1080/0951192X.2012.684706

    Google Scholar 

  • Mathieson JL, Summers JD (2010) Complexity metrics for directional node-link system representations: theory and applications. In: ASME international design engineering technical conferences and computers and information in engineering conference. ASME, Montreal, Canada, pp DETC2010–28561

    Google Scholar 

  • Mathieson JL, Summers JD (2009) Relational DSMs in connectivity complexity measurement. In: Proceedings of the 11th international DSM conference, pp 15–26

    Google Scholar 

  • McGown A, Green G, Rodgers PA (1998) Visible ideas: information patterns of conceptual sketch activity. Des Stud 19:431–453

    Article  Google Scholar 

  • McKoy FL, Vargas-Hernandez N, Summers JD, Shah JJ (2001) Influence of design representation on effectiveness of idea generation

    Google Scholar 

  • Moore RA. Romero DA, Paredis CJJ (2014) Value-based global optimization. J Mech Des 136:041003. doi:10.1115/1.4026281

    Google Scholar 

  • Namouz E, Summers JD (2014) Comparison of graph generation methods for structural complexity based assembly time estimation. ASME Trans J Comput Inf Sci Eng 14:021003. doi:10.1115/1.4026293

    Article  Google Scholar 

  • Oman S, Tumer IY, Stone R (2014) Reducing the subjectivity in creativity assessment. In: Proceedings of ASME IDETC/CIE. ASME, Portland, p V007T07A043

    Google Scholar 

  • Oman SK, Tumer IY, Wood KL, Seepersad C (2012) A comparison of creativity and innovation metrics and sample validation through in-class design projects. Res Eng Des 24:65–92. doi:10.1007/s00163-012-0138-9

    Article  Google Scholar 

  • Orbay G, Fu L, Kara LB (2015) Deciphering the influence of product shape on consumer judgments through geometric abstraction. J Mech Des 137:081103. doi:10.1115/1.4030206

    Article  Google Scholar 

  • Owensby JE, Namouz EZ, Shanthakumar A, Summers JD (2012) Representation: extracting mate complexity from assembly models to automatically predict assembly times. In: ASME international design engineering technical conferences and computers and information in engineering conference. ASME, Chicago, IL, pp DETC2012–70995

    Google Scholar 

  • Owensby JE, Summers JD (2014) Assembly time estimation: assembly mate based structural complexity metric predictive modelling. ASME Trans J Comput Inf Sci Eng. doi:10.1115/1.4025808

    Google Scholar 

  • Pahl G, Beitz W, Wallace K, Blessing L (2007) Engineering design: a systematic approach, 3rd edn. Springer-Verlag, London Limited, London

    Book  Google Scholar 

  • Park YS (2014) Theory and methodology for forming creative design teams in a globally distributed and culturally diverse environment

    Google Scholar 

  • Phukan A, Kalava M, Prabhu V (2005) Complexity metrics for manufacturing control architecture based on software and information flow. Comput Ind Eng 49:1–20

    Article  Google Scholar 

  • Pugh S (1991) Total design: integrated methods for successful product engineering. Addison-Wesley Publishing Company, Workingham

    Google Scholar 

  • Pugh S, Clausing D (1996) Creating innovtive products using total design: the living legacy of Stuart Pugh. Addison-Wesley Longman Publishing Co., Inc

    Google Scholar 

  • Ramachandran R, Caldwell BW, Mocko GM (2011) A user study to evaluate the functional model and function interaction model for concept generation. In: International design engineering technical conferences and computers and information in engineering conference. ASME, Washington, DC, p DETC–47660

    Google Scholar 

  • Ren Y, Papalambros PY (2011) A design preference elicitation query as an optimization process. J Mech Des 133:111004. doi:10.1115/1.4005104

    Article  Google Scholar 

  • Roser CH (2000) A flexible design methodology

    Google Scholar 

  • Saaty TL (1980) The analytical hierarchical process

    Google Scholar 

  • Schmidt LC, Vargas-Hernandez N, Kremer G, Linsey JS (2010) Pilot of systematic ideation study with lessons learned. In: International design engineering technical conferences and computers and information in engineering conference. ASME, Montreal, Canada, pp DETC2010–28785

    Google Scholar 

  • Sedgewick R (1990) Algorithms in C++. Addison-Wesley

    Google Scholar 

  • Sen C, Ameri F, Summers JD (2010) An entropic method for sequencing discrete design decisions. J Mech Des 132:101004

    Article  Google Scholar 

  • Shafiei-Monfared S, Jenab K (2012) A novel approach for complexity measure analysis in design projects. J Eng Des 23:185–194

    Article  Google Scholar 

  • Shah JJ (2005) Identification, measurement and development of design skills in engineering education. In: Samuel A, Lewis W (eds) Proceedings of the 15th international conference on engineering design (ICED05). Melbourne, Australia, p DS35_557.1

    Google Scholar 

  • Shah JJ, Kulkarni SV, Vargas-Hernandez N (2000) Evaluation of idea generation methods for conceptual design: effectiveness metrics and design of experiments. J Mech Des 122:377–384. doi:10.1115/1.1315592

    Article  Google Scholar 

  • Shah JJ, Millsap RE, Woodward J, Smith SM (2012) Applied tests of design skills—part 1: divergent thinking. J Mech Des 134:021005. doi:10.1115/1.4005594

    Article  Google Scholar 

  • Shah JJ, Runger G (2011) Misuse of information-theoretic dispersion measures as design complexity metrics. In: ASME international design engineering technical conferences and computers and information in engineering conference. ASME, Washington, DC, p DETC2011/DTM–48295

    Google Scholar 

  • Shah JJ, Smith SM, Vargas-Hernandez N (2003) Metrics for measuring ideation effectiveness. Des Stud 24:111–134

    Article  Google Scholar 

  • Shah JJ, Woodward J, Smith SM (2013) Applied tests of design skills—part II: visual thinking. J Mech Des 135:71004. doi:10.1115/1.4024228

    Article  Google Scholar 

  • Simon H (1998) The sciences of the artificial. MIT Press, Cambridge

    Google Scholar 

  • Singh G, Balaji S, Shah JJ, et al (2012) Evaluation of network measures as complexity metrics. In: ASME international design engineering technical conferences and computers and information in engineering conference. ASME, Chicago, IL, pp DETC2012–70483

    Google Scholar 

  • Sinha K, de Weck OL (2013a) A network-based structural complexity metric for engineered complex systems. In: 2013 IEEE international on systems conference (SysCon), pp 426–430

    Google Scholar 

  • Sinha K, de Weck OL (2013b) Structural complexity quantification for engineered complex systems and implications on system architecture and design. In: ASME 2013 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers, pp V03AT03A044–V03AT03A044

    Google Scholar 

  • Sonalkar N, Jung M, Mabogunje A, Leifer L (2014) A structure for design theory. In: Chakrabarti A, Blessing LTM (eds) An anthology of theories and models of design. Springer, London, pp 67–81

    Chapter  Google Scholar 

  • Stahovich TF (2000) LearnIT: an instance-based approach to learning and reusing design strategies. J Mech Des 122:249–256

    Article  Google Scholar 

  • Suh N (1999) A theory of complexity, periodicity, and the design axioms. Res Eng Des 11:116–131

    Article  Google Scholar 

  • Suh N (2001) Axiomatic design: advances and applications. Oxford University Press, New York

    Google Scholar 

  • Summers JD, Ameri F (2008) An algorithm for assessing design complexity through a connectivity view

    Google Scholar 

  • Summers JD, Miller MG, Mathieson JL et al (2014) Manufacturing assembly time estimation using structural complexity metric trained artificial neural networks. J Comput Inf Sci Eng 14:11005. doi:10.1115/1.4025809

    Article  Google Scholar 

  • Summers JD, Shah JJ (2010) Mechanical engineering design complexity metrics: size, coupling, and solvability. J Mech Des 132:21004

    Article  Google Scholar 

  • Tilstra A, Seepersad CC, Wood KL (2009) A Systematic method of product design for flexibility for future evolution. In: Proceedings of 2009 NSF engineering research and innovation conference, Honolulu, HI

    Google Scholar 

  • Tovares N, Boatwright P, Cagan J (2014) Experiential conjoint analysis: an experience-based method for eliciting, capturing, and modeling consumer preference. J Mech Des 136:101404. doi:10.1115/1.4027985

    Article  Google Scholar 

  • Tovey M, Porter S, Newman R (2003) Sketching, concept development and automotive design. Des Stud 24:135–153. doi:10.1016/S0142-694X(02)00035-2

    Article  Google Scholar 

  • Tucker CS, Kim HM (2011) Trend mining for predictive product design. J Mech Des 133:111008. doi:10.1115/1.4004987

    Article  Google Scholar 

  • Ulrich KT, Eppinger SD (2004) Product design and development. McGraw-Hill, Boston

    Google Scholar 

  • Ulrich KT, Eppinger SD (1988) Product design and development, 1995

    Google Scholar 

  • United States Government Accountability Office (2008) Defense Acquisitions: Assessments of Selected Weapon Programs (GAO-08-467SP)

    Google Scholar 

  • van der Lugt R (2005) How sketching can affect the idea generation process in design group meetings. Des Stud 26:101–122. doi:10.1016/j.destud.2004.08.003

    Article  Google Scholar 

  • Vargas-Hernandez N, Shah JJ, Smith SM (2010) Understanding design ideation mechanisms through multilevel aligned empirical studies. Des Stud 31:382–410. doi:10.1016/j.destud.2010.04.001

    Article  Google Scholar 

  • Varma D, Trachterberg E (1990) On the estimation of logic complexity for design automation applications. In: International conference on computer design: VLSI in computers and processors, Cambridge, MA

    Google Scholar 

  • Wan J, Krishnamurty S (2001) Learning-based preference modeling in engineering design decision-making. J Mech Des 123:191. doi:10.1115/1.1361061

    Article  Google Scholar 

  • Wassenaar HJ, Chen W (2003) An approach to decision-based design with discrete choice analysis for demand modeling. J Mech Des 125(3):490. doi:10.1115/1.1587156

    Google Scholar 

  • Wassenaar HJ, Chen W, Cheng J, Sudjianto A (2005) Enhancing discrete choice demand modeling for decision-based design. J Mech Des 127:514. doi:10.1115/1.1897408

    Article  Google Scholar 

  • Weber C (2005) What is complexity. In: Proceedings of 15th international conference on engineering design. The Design Society, p DS35_485.49

    Google Scholar 

  • Westmoreland S, Ruocco A, Schmidt L (2011) Analysis of capstone design reports: visual representations. J Mech Des 133:051010. doi:10.1115/1.4004015

    Article  Google Scholar 

  • White C, Wood K, Jensen D (2012) From brainstorming to C-sketch to principles of historical innovators: ideation techniques to enhance student creativity. J STEM Educ 13:12

    Google Scholar 

  • Wilde DJ (2008) Teamology: the construction and organization of effective teams. Springer

    Google Scholar 

  • Wilde DJ (2011) Jung’s personality theory quantified. Springer Science & Business Media

    Google Scholar 

  • Wood M, Chen P, Fu K, et al (2012) The role of design team interaction structure on individual and shared mental models. In: Design Computing and Cognition2, College Station, TX, USA

    Google Scholar 

  • Worinkeng E, Summers JD, Joshi S (2013) Can a pre-sketching activity improve idea generation? In: Smart product engineering. Springer, pp 583–592

    Google Scholar 

  • Yang MC (2003) Concept generation and sketching: correlations with design outcome. ASME international design engineering technical conferences and computers and information in engineering conference. ASME, Chicago, IL, pp 829–834

    Google Scholar 

  • Yang MC (2009) Observations on concept generation and sketching in engineering design. Res Eng Des 20:1–11

    Article  Google Scholar 

  • Yang MC, Cham JG (2007) An analysis of sketching skill and its role in early stage engineering design. J Mech Des 129:476–482. doi:10.1115/1.2712214

    Article  Google Scholar 

  • Zuse H (1991) Software complexity: measures and methods. Walter de Gruyter and Co., New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical and Aerospace Engineering, Ohio State University, Scott Lab E343, 201 W. 19th Avenue, Columbus, OH, 43210, USA

    Jami Shah

  2. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 813 Ferst Drive, Atlanta, GA, 30332, USA

    Mahmoud Dinar

  3. Mechanical Engineering, Clemson University, Clemson, South Carolina, USA

    Joshua D. Summers

  4. Mechanical and Aerospace Eng Arizona State University, Tempe, Arizona, 85287-6016, USA

    Yong-Seok Park

Authors
  1. Mahmoud Dinar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua D. Summers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jami Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong-Seok Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jami Shah .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Philip Cash

  2. Swiss Federal Institute of Tech. Zürich, Zurich, Switzerland

    Tino Stanković

  3. University of Zagreb, Zagreb, Croatia

    Mario Štorga

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Dinar, M., Summers, J.D., Shah, J., Park, YS. (2016). Evaluation of Empirical Design Studies and Metrics. In: Cash, P., Stanković, T., Štorga, M. (eds) Experimental Design Research. Springer, Cham. https://doi.org/10.1007/978-3-319-33781-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33781-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33779-1

  • Online ISBN: 978-3-319-33781-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation